Can Qwen 2.5 Coder 1.5B run on NVIDIA A16 64GB?

YES — Runs Great

B59Good

Estimated from fit model

Qwen 2.5 Coder 1.5B needs ~8.9 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.9 GB, 21.0 tok/s, Runs well

8.9 GB required64.0 GB available

14% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

33K

Memory

8.9 GB / 64.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 1.5B on NVIDIA A16 64GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	21.0 tok/s	5029 ms	33K
Coding	B	Runs well	21.0 tok/s	9219 ms	33K
Agentic Coding	B	Runs well	21.0 tok/s	13410 ms	33K
Reasoning	B	Runs well	21.0 tok/s	10895 ms	33K
RAG	B	Runs well	21.0 tok/s	16762 ms	33K

Quantization options

How Qwen 2.5 Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	B59
Q3_K_S	3	0.7 GB	Low	B59
NVFP4	4	0.8 GB	Medium	B59
Q4_K_M	4	0.9 GB	Medium	B59
Q5_K_M	5	1.1 GB	High	B59
Q6_K	6	1.2 GB	High	B59
Q8_0	8	1.6 GB	Very High	B59
F16Best for your GPU	16	3.1 GB	Maximum	B59

Get started

Copy-paste commands to run Qwen 2.5 Coder 1.5B on your machine.

Run

ollama run qwen2.5-coder:1.5b

Upgrade options

Hardware that runs Qwen 2.5 Coder 1.5B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)

This setup is broadly balanced for this model.21 tok/s decode

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+64)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

NVIDIA DGX Spark 128GBNVIDIA upgrade

128 GB Unified (+64)

Adds memory headroom for longer context windows and future model growth.21 tok/s decode

Adds memory headroom for longer context windows and future model growth.

Frequently asked questions

Can NVIDIA A16 64GB run Qwen 2.5 Coder 1.5B?

Yes, NVIDIA A16 64GB can run Qwen 2.5 Coder 1.5B with a B grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Qwen 2.5 Coder 1.5B need?

Qwen 2.5 Coder 1.5B (1.5B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 1.5B?

The recommended quantization for Qwen 2.5 Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 1.5B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen 2.5 Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Qwen 2.5 Coder 1.5B for coding?

For coding workloads, Qwen 2.5 Coder 1.5B on NVIDIA A16 64GB receives a B grade with 21.0 tok/s and 33K context.

What context window can Qwen 2.5 Coder 1.5B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen 2.5 Coder 1.5B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Qwen 2.5 Coder 1.5B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-1.5b-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

21.0 tok/s

5029 ms

33K

Coding

Runs well

21.0 tok/s

9219 ms

33K

Agentic Coding

Runs well

21.0 tok/s

13410 ms

33K

Reasoning

Runs well

21.0 tok/s

10895 ms

33K

RAG

Runs well

21.0 tok/s

16762 ms

33K

Quantization options

How Qwen 2.5 Coder 1.5B (1.5B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	B59
Q3_K_S	3	0.7 GB	Low	B59
NVFP4	4	0.8 GB	Medium	B59
Q4_K_M	4	0.9 GB	Medium	B59
Q5_K_M	5	1.1 GB	High	B59
Q6_K	6	1.2 GB	High	B59
Q8_0	8	1.6 GB	Very High	B59
F16Best for your GPU	16	3.1 GB	Maximum	B59

Frequently asked questions

Can NVIDIA A16 64GB run Qwen 2.5 Coder 1.5B?

Yes, NVIDIA A16 64GB can run Qwen 2.5 Coder 1.5B with a B grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Qwen 2.5 Coder 1.5B need?

Qwen 2.5 Coder 1.5B (1.5B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 1.5B?

The recommended quantization for Qwen 2.5 Coder 1.5B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 1.5B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen 2.5 Coder 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Qwen 2.5 Coder 1.5B for coding?

For coding workloads, Qwen 2.5 Coder 1.5B on NVIDIA A16 64GB receives a B grade with 21.0 tok/s and 33K context.

What context window can Qwen 2.5 Coder 1.5B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Qwen 2.5 Coder 1.5B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.